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    "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
    "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/Algorithms/ntl_linear_fit.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
    "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Algorithms/ntl_linear_fit.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
    "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Algorithms/ntl_linear_fit.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
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   "source": [
    "## Install Earth Engine API and geemap\n",
    "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
    "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Installs geemap package\n",
    "import subprocess\n",
    "\n",
    "try:\n",
    "    import geemap\n",
    "except ImportError:\n",
    "    print('Installing geemap ...')\n",
    "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "import geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an interactive map \n",
    "The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "Map = geemap.Map(center=[40,-100], zoom=4)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Earth Engine Python script "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Earth Engine dataset\n",
    "# Compute the trend of nighttime lights from DMSP.\n",
    "\n",
    "# Add a band containing image date as years since 1990.\n",
    "def createTimeBand(img):\n",
    "  year = img.date().difference(ee.Date('1990-01-01'), 'year')\n",
    "  return ee.Image(year).float().addBands(img)\n",
    "\n",
    "# function createTimeBand(img) {\n",
    "#   year = img.date().difference(ee.Date('1990-01-01'), 'year')\n",
    "#   return ee.Image(year).float().addBands(img)\n",
    "# }\n",
    "\n",
    "# Fit a linear trend to the nighttime lights collection.\n",
    "collection = ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4') \\\n",
    "    .select('avg_vis') \\\n",
    "    .map(createTimeBand)\n",
    "fit = collection.reduce(ee.Reducer.linearFit())\n",
    "\n",
    "# Display a single image\n",
    "Map.addLayer(ee.Image(collection.select('avg_vis').first()),\n",
    "         {'min': 0, 'max': 63},\n",
    "         'stable lights first asset')\n",
    "\n",
    "# Display trend in red/blue, brightness in green.\n",
    "Map.setCenter(30, 45, 4)\n",
    "Map.addLayer(fit,\n",
    "         {'min': 0, 'max': [0.18, 20, -0.18], 'bands': ['scale', 'offset', 'scale']},\n",
    "         'stable lights trend')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Earth Engine data layers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
    "Map"
   ]
  }
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